While computational models are playing an increasingly important role in developmental psychology, at least one lesson from robotics is still being learned: modeling epigenetic processes often requires simulating an embodied, autonomous organism. This paper first contrasts prevailing models of infant cognition with an agent-based approach. A series of infant studies by Baillargeon (1986; Baillargeon & DeVos, 1991) is described, and an eye-movement model is then used to simulate infants' visual activity in this study. I conclude by describing three behavioral predictions of the eyemovement model, and discussing the implications of this work for infant cognition research.

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Research in embodied cognitive science emphasizes that a close interaction of brain, body and environment is central to the emergence of cognitive processes. Much work on embodied artificial intelligence has therefore shifted focus from purely computational modeling to autonomous mobile robotics. Many researchers emphasize the importance of working with real robots rather than simulations which usually cannot fully capture the complexities of the physical world. However, from a cognitive science point of view, robot simulations nevertheless have an important, complementary role to play, due to the fact that in many cases they allow for more extensive, systematic experimentation as well as for experiments, e.g. with evolving robot morphologies, that can only be carried out in very limited form on real robots. Furthermore, it will be argued in this paper, robot simulations are very useful tools in experimentation with active adaptation of non-trivial environments, an aspect that is still largely ignored in much embodied artificial intelligence research. 1

The violation-of-expectation paradigm investigates infants' physical knowledge by exploiting their tendency to look longer at events that are surprising, unexpected, or physically impossible. The current simulation study examines the role of prediction as a fundamental component of infants' expectations in physical-knowledge studies. A recurrent network is presented with a computer-animated version of Baillargeon’s “car study ” (1986; Baillargeon & DeVos, 1991), in which a car rolls down a ramp and behind a screen. After learning to predict the outcome of a training event, the model is then tested on possible and impossible events from the same study. During testing, the model successfully predicts only superficial features of the test events. These results are used to argue for the necessity of prior physical knowledge, and perhaps also a built-in capacity for mental representation, in order for a prediction system to work.

cognitive science: Integrative approaches to learning and development, Neurocomputing, doi:10.1016/j.neucom.2006.06.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Theory grounding is suggested as a way to address the unresolved cognitive science issues of systematicity and productivity. Theory grounding involves grounding the theory skills and knowledge of an embodied artificially intelligent (AI) system by developing theory skills and knowledge from the bottom up. It is proposed that theory grounded AI systems should be patterned after the psychological developmental stages that infants and young children go through in acquiring naïve theories. Systematicity and productivity are properties of certain representational systems indicating the range of representations the systems can form. Systematicity and productivity are likely outcomes of theory grounded AI systems because systematicity and productivity are theoretical concepts. Theory grounded systems should be well oriented to acquire and develop these theoretical concepts.

The capacity for infants to form mental representations of hidden or occluded objects can be decomposed into two tasks: one process that identifies salient objects and a second complementary process that identifies salient locations. This functional decomposition is supported by the distinction between dorsal and ventral extrastriate visual processing in the primate visual system. This approach is illustrated by presenting an eye-movement model that incorporates both dorsal and ventral processing streams and by using the model to simulate infants ’ reactions to possible and impossible events from an infant looking-time study (R. Baillargeon, “Representing the existence and the location of hidden objects: object permanence in 6- and 8-month-old infants”, Cognition, 23, pp. 21–41, 1986.). As expected, the model highlights how the dorsal system is sensitive to the location of a key feature in these events (i.e. the location of an obstacle), whereas the ventral system responds equivalently to the possible and impossible events. These results are used to help explain infants’reactions in looking-time studies. Keywords: Object representations; Dorsal–ventral model; Infant perception 1.

Abstract. Schlesinger (2003, Adaptive Behavior, 11: 97–107) recently proposed a model of eye movements as a tool for investigating infants ’ visual expectations. In the present study, this gazedirection model was evaluated by: (a) generating a set of predictions concerning how infants distribute their attention during possible and impossible events; and (b) testing these predictions in a replication of Baillargeon’s ‘car study ’ (Baillargeon, 1986, Cognition, 23: 21–41, Baillargeon and DeVos, Child Development, 62: 1227–1246). We found that the model successfully predicts general features of infants ’ gaze direction, but not specific differences obtained during the possible and impossible events. The implications of these results for infant cognition research and theory are discussed.

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